/**
* @param threshold similarity threshold
* @param userCorrelation similarity metric
* @param dataModel data Model
* @param samplingRate percentage of users to consider when building Neighborhood -- decrease to
* trade quality for performance
* @throws IllegalArgumentException if threshold or samplingRate is {@link Double#NaN},
* or if samplingRate is not positive and less than or equal to 1.0, or if userCorrelation
* or dataModel are <code>null</code>
* @since 1.3
*/
public ThresholdUserNeighborhood(double threshold,
UserCorrelation userCorrelation,
DataModel dataModel,
double samplingRate)
: base(userCorrelation, dataModel, samplingRate)
{
if (Double.IsNaN(threshold))
{
throw new ArgumentException("threshold must not be NaN");
}
this.cache = new SoftCache <Object, ICollection <User> >(new Retriever(this, threshold), dataModel.GetNumUsers());
}
/// <summary>
/// construct a NearestNUserNeighborhood
/// </summary>
/// <param name="n">n Neighborhood size</param>
/// <param name="userCorrelation">nearness metric</param>
/// <param name="dataModel">data Model</param>
/// <param name="samplingRate">percentage of users to consider when building Neighborhood -- decrease to
/// trade quality for performance</param>
public NearestNUserNeighborhood(int n,
UserCorrelation userCorrelation,
DataModel dataModel,
double samplingRate)
: base(userCorrelation, dataModel, samplingRate)
{
if (n < 1)
{
throw new ArgumentException("n must be at least 1");
}
this.cache = new SoftCache <Object, ICollection <User> >(new Retriever(this, n), dataModel.GetNumUsers());
}
public CachingRecommender(Recommender recommender)
{
if (recommender == null)
{
throw new ArgumentNullException("Recommender is null");
}
this.recommender = recommender;
this.maxHowMany = new AtomicInteger(1);
// Use "num users" as an upper limit on cache size. Rough guess.
int numUsers = recommender.DataModel.GetNumUsers();
this.recommendationCache =
new SoftCache <Object, Recommendations>(
new RecommendationRetriever(this.recommender, this.maxHowMany),
numUsers);
this.estimatedPrefCache =
new SoftCache <Pair <object, object>, Double>(new EstimatedPrefRetriever(this.recommender), numUsers);
this.refreshLock = new ReentrantLock();
}
public ZScore()
{
this.meanAndStdevs = new SoftCache <User, RunningAverageAndStdDev>(new MeanStdevRetriever());
Refresh();
}
public AveragingPreferenceInferrer(DataModel dataModel)
{
averagePreferenceValue = new SoftCache <User, Double>(RETRIEVER, dataModel.GetNumUsers());
Refresh();
}
